Better to learn the same small set for multiple epochs then go to the next or learn from each one time repeatedly for multiple times?
Small set for multiple epochs causes overfit, so expose entire data in one epoch, then augment (change a littel bit), and train again. Use small amount of dropout also.

I have a big dataset (300000 of images after augumentation) and I've splitted it into 10 parts, because I can't convert the images into a numpy and save it, the file would be too large.


Do not store augmented data, just do augment with function right after next epoch and overwrite the dataset array